Once-for-All (OFA) training enables a single super-net to generate multiple sub-nets tailored to diverse deployment scenarios, supporting flexible trade-offs among accuracy, robustness, and model-size without retraining. However, as the number of supported sub-nets increases, excessive parameter sharing in the backbone limits representational capacity, leading to degraded calibration and reduced overall performance. To address this, we propose SOLAR (Switchable Output Layer for Accuracy and Robustness in Once-for-All Training), a simple yet effective technique that assigns each sub-net a separate classification head. By decoupling the logit learning process across sub-nets, the Switchable Output Layer (SOL) reduces representational interference and improves optimization, without altering the shared backbone. We evaluate SOLAR on five datasets (SVHN, CIFAR-10, STL-10, CIFAR-100, and TinyImageNet) using four super-net backbones (ResNet-34, WideResNet-16-8, WideResNet-40-2, and MobileNetV2) for two OFA training frameworks (OATS and SNNs). Experiments show that SOLAR outperforms the baseline methods: compared to OATS, it improves accuracy of sub-nets up to 1.26%, 4.71%, 1.67%, and 1.76%, and robustness up to 9.01%, 7.71%, 2.72%, and 1.26% on SVHN, CIFAR-10, STL-10, and CIFAR-100, respectively. Compared to SNNs, it improves TinyImageNet accuracy by up to 2.93%, 2.34%, and 1.35% using ResNet-34, WideResNet-16-8, and MobileNetV2 backbones (with 8 sub-nets), respectively. The code of SOLAR is publicly available at: https://github.com/NAIL-UH/SOLAR and its website can be accessed at https://saktx.github.io/solar.github.io/.

Deploying high-performance object detectors on TinyML platforms poses significant challenges due to tight hardware constraints and the modular complexity of modern detection pipelines. Neural Architecture Search (NAS) offers a path toward automation, but existing methods either restrict optimization to individual modules—sacrificing cross-module synergy—or require global searches that are computationally intractable. We propose ELASTIC (Efficient Once for AlLIterAtiveSearch for ObjecTDetectIon on MiCrocontrollers), a unified, hardware-aware NAS framework that alternates optimization across modules (e.g., backbone, neck, and head) in a cyclic fashion. ELASTIC introduces a novelPopulation Passthroughmechanism in evolutionary search that retains high-quality candidates between search stages, yielding faster convergence, up to an 8% final mAP gain, and eliminates search instability observed without population passthrough. In a controlled comparison, empirical results show ELASTIC achieves +4.75% higher mAP and 2× faster convergence than progressive NAS strategies on SVHN, and delivers a +9.09% mAP improvement on PascalVOC given the same search budget. ELASTIC achieves 72.3% mAP on PascalVOC, outperforming MCUNET by 20.9% and TinyissimoYOLO by 16.3%. When deployed on MAX78000/MAX78002 microcontrollers, ELASTICderived models outperform Analog Devices’ TinySSD baselines, reducing energy by up to 71.6 %, lowering latency by up to 2.4×, and improving mAP by up to 6.99 percentage points across multiple datasets. The experimental videos and codes are available on the project website1.

This paper addresses a distributed leader-follower formation control problem for a group of agents, each using a body-fixed camera with a limited field of view (FOV) for state estimation. The main challenge arises from the need to coordinate the agents' movements with their cameras' FOV to maintain visibility of the leader for accurate and reliable state estimation. To address this challenge, we propose a novel perception-aware distributed leader-follower safe control scheme that incorporates FOV limits as state constraints. A Control Barrier Function (CBF) based quadratic program is employed to ensure the forward invariance of a safety set defined by these constraints. Furthermore, new neural network based and double bounding boxes based estimators, combined with temporal filters, are developed to estimate system states directly from real-time image data, providing consistent performance across various environments. Comparison results in the Gazebo simulator demonstrate the effectiveness and robustness of the proposed framework in two distinct environments.

Artificial Intelligence (AI) is crucial to numerous functions required for driving automation systems, including the computer vision techniques used to detect the roadway environment and make real-time decisions. However, the images used as inputs to the AI system may be maliciously perturbed, or manipulated, causing the AI system to make an incorrect classification. In this study, we examined humans’ perception of the AI’s computer vision capability of classifying various road sign images, including the original images, images with two different types of malicious attacks, and images that are scrambled randomly at the pixel level. Our results showed that participants rated the AI agent to be less capable than themselves of classifying the road signs. However, they overestimated the AI’s computer vision capability for correctly classifying images with malicious attacks that should cause the AI system to misclassify the image. These findings suggest that people lack an accurate understanding of the vulnerabilities of AI computer vision technologies and tend to overtrust AI in driving automation systems.

Energy is an essential, but often forgotten aspect in large-scale federated systems. As most of the research focuses on tackling computational and statistical heterogeneity from the machine learning algorithms, the impact on the mobile system still remains unclear. In this paper, we design and implement an online optimization framework by connecting asynchronous execution of federated training with application co-running to minimize energy consumption on battery-powered mobile devices. From a series of experiments, we find that co-running the training process in the background with foreground applications gives the system a deep energy discount with negligible performance slowdown. Based on these results, we first study an offline problem assuming all the future occurrences of applications are available, and propose a dynamic programming-based algorithm. Then we propose an online algorithm using the Lyapunov framework to explore the solution space via the energy-staleness trade-off. The extensive experiments demonstrate that the online optimization framework can save over 60% energy with 3 times faster convergence speed compared to the previous schemes.

Antiphishing aid systems, among other automated systems, are not perfectly reliable. Automated systems can make errors, thereby resulting in false alarms or misses. An automated system's capabilities need to be communicated to the users to maintain proper user trust. System capabilities can be learned through an explicit description or from experience. Using a phishing-detection system as a testbed in this article, we systematically varied automation error type and the method of communicating system reliability in a factorial design and measured their effects on human performance and trust in the automation. Participants were asked to classify emails as legitimate or phishing with assistance from the phishing-detection system. The results from 510 participants suggest that learning through experience with feedback improved trust calibration for both objective and subjective trust measures in most conditions. Moreover, false alarms lowered trust more than misses for both unreliable and reliable systems, and false alarms turned out to be beneficial for proper trust calibration when using unreliable systems. Design implications of the results include using feedback whenever possible and choosing false alarms over misses for unreliable systems.